Drug-Disease Association Prediction Using Heterogeneous Networks for Computational Drug Repositioning.

Drug repositioning, which involves the identification of new therapeutic indications for approved drugs, considerably reduces the time and cost of developing new drugs. Recent computational drug repositioning methods use heterogeneous networks to identify drug-disease associations. This review reveals existing network-based approaches for predicting drug-disease associations in three major categories: graph mining, matrix factorization or completion, and deep learning. We selected eleven methods from the three categories to compare their predictive performances. The experiment was conducted using two uniform datasets on the drug and disease sides, separately. We constructed heterogeneous networks using drug-drug similarities based on chemical structures and ATC codes, ontology-based disease-disease similarities, and drug-disease associations. An improved evaluation metric was used to reflect data imbalance as positive associations are typically sparse. The prediction results demonstrated that methods in the graph mining and matrix factorization or completion categories performed well in the overall assessment. Furthermore, prediction on the drug side had higher accuracy than on the disease side. Selecting and integrating informative drug features in drug-drug similarity measurement are crucial for improving disease-side prediction.

Cellulase production from Pseudoalteromonas sp. NO3 isolated from the sea squirt Halocynthia rorentzi.

Pseudoalteromonas sp. NO3 was isolated from the hemolymph of diseased sea squirts (Halocynthia rorentzi) with symptoms of soft tunic syndrome. The strain was found to produce an extracellular cellulase (CelY) that consisted of a 1,476 bp open reading frame encoding 491 amino acid residues with an approximate molecular mass of 52 kDa. Homologies of the deduced amino acid sequence of celY with the products of the celA, celX, celG and cel5Z genes were 92.6, 93.3, 92.6, and 59.1%, respectively. Additionally, CelY had 50-80% remnant catalytic activity at temperatures of 10-20 degrees C. Highest carboxymethyl cellulose (CMC) hydrolysis was observed at pH 8.0 and 40 degrees C. CMC activity was determined by zymogram active staining and different degraded product profiles for CelY were obtained when cellotetraose, cellopentaose, and CMC were used as substrates. This study identified a transglycosylation activity in CelY that allows the enzyme to digest G4 to G2 and G3 without the production of G1.